Hybrid WT-PSO based Neural Networks for Single Step-Ahead Wind Power Prediction for Ontario Electricity Market
Bibliographic record
Abstract
Wind Power forecasting is an important subject of concern for reliable operations of grid and it has been studied from different points of views of both accuracy and reliability. So with an aim of improvement in prediction accuracy this paper presents a hybrid wind power prediction machine for Ontario Electricity Market (OEM) on single step ahead basis in which Wavelet Transform (WT) is used for pre- processing of input wind power data, then the pre-processed data is trained by neural networks. In this initially, the parameters of neural networks (biases & weights) are initialized as random &then at second stage are optimized by Particle Swarm Optimization (PSO) base training algorithm. The varying time series input training data patterns are used in order to remove the overtraining & over-fitting problem so that the maximum accuracy is achieved. The results of proposed method are compared with Naive Predictor, Feed Forward Neural Networks (FFNN) & Particle Swarm Optimization based Neural Network (PSONN) and is presented in the form of comparative tables on Mean absolute error (MAE) & mean absolute percentage error (MAPE) scale with emphasis on weekly as well as monthly predictions. The data used by proposed model for estimation is collected from Ontario Electricity Market for the year 2009-12 and tested for such a long period of one year on single step ahead basis. It is found that the accuracy of proposed model is far better than the other models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".